quantitative image analysis for geologic core description

Journal of Sedimentary Research, 2017, v. 87, 460–485

Research Article

DOI: http://dx.doi.org/10.2110/jsr.2017.25

QUANTITATIVE IMAGE ANALYSIS FOR GEOLOGIC CORE DESCRIPTION

ROGER J. BARNABY

DigitalStratigraphy, 415 West 15th Street, Houston, Texas 77008, U.S.A.

e-mail: [email protected]

ABSTRACT: Many basic rock properties—such as lithology, bedding, grain size, sorting, and porosity—are expressedin geologic cores by changes in color, brightness, and texture. Quantitative descriptive rock properties can thus bederived from digital core images. Despite the widespread availability of high-resolution core images and imageanalysis software, these data are underutilized by geoscientists tasked with describing core.This paper demonstrates the application of image analysis for quantitative core description using examples from

three different carbonate reservoirs: (1) evaporite-rich dolostone from the First Eocene, Kuwait–Saudi ArabiaPartitioned Zone, (2) vuggy dolostone from the Cretaceous Toca Formation, offshore Angola, (3) thin-beddedlimestone and mudrock from the Ordovician Utica Formation, Ohio, USA. In each example, quantitative data areextracted from core images using ImageJ or WellCAD software. The image-derived descriptive parameters areconsistent with petrophysical log and core data, supporting the validity of this approach to core description.Image-analysis-guided core description offers many advantages over traditional hand-drawn core description: 1)

hand-drawn core descriptions tend to be qualitative and core-log integration is difficult and imprecise, whereas imageanalysis generates quantitative descriptive data that are directly comparable with petrophysical datasets; 2) imageanalysis can characterize fine-scale geologic heterogeneity that is difficult or impossible to resolve using log and coreplug data and hand-drawn core descriptions; 3) image analysis allows geologists to generate preliminary descriptionsprior to actual core viewing, a more efficient workflow that minimizes time expended in offsite core viewing, perhapsin remote locations with limited time available; 4) integration of image-derived core data with petrophysical log andcore data allows rigorous evaluation of core data quality—before, during, and after the process of core description.Image analysis thus provides a valuable tool for geoscientists to efficiently generate quantitative, petrophysically

significant core descriptions.

INTRODUCTION

Digital images—white light (WL) and ultraviolet (UV) color photo-

graphs and X-ray computed tomography (CT) scans—are routinely

acquired from whole and slabbed cores. These images contain detailed

information on many basic rock properties. This paper demonstrates how

image analysis can extract quantitative data that allow geologists to

generate core descriptions that are integrated with log and core data.

Advances in high-performance Windows PC tablets (8–16 GB RAM, 64

bit, i7 processors, solid-state hard drives) and WellCAD and ImageJ

software provide an ideal platform for geoscientists to generate integrated

core descriptions. Geologic descriptions are drafted using a stylus on a PC

tablet, directly on top of or adjacent to images and petrophysical core and

log data. The initial image analysis, data integration, and preliminary core

description can be performed using a computer prior to core viewing,

optimizing the time expended at offsite core facilities, which may require

expensive travel to remote and/or dangerous locations. Further image

analysis can be performed as the core is being described.

The resulting quantitative core descriptions can be directly compared

with log- and core-based petrophysical data. Image analysis provides high-

resolution information on fine-scale geological heterogeneity that is

unavailable from standard wireline and core analyses and traditional core

descriptions. Image-derived quantitative core descriptions are useful to

evaluate core datasets for possible errors or data problems. Lastly, although

hand-drawn geologic core descriptions may be of interest to other

geologists, they have limited utility for coworkers in other disciplines, such

as petrophysicists, reservoir modelers, and petroleum engineers who

require digital geologic descriptions and interpretations.

To illustrate the application of image analysis to geologic core

description, this paper describes three examples from heterogeneous

carbonate reservoirs that are difficult to quantitatively characterize using

log and core data and traditional hand-drawn geologic descriptions. The

first example is from the First Eocene Formation in Wafra Field, Kuwait–

Saudi Arabia Partitioned Zone. The objective of this study was to quantify

possible changes to the mineralogy, reservoir quality, and fluid saturation

due to steamflooding. The second example, utilizing vuggy Toca

dolostones from offshore Angola, demonstrates how image analysis of

CT scans can characterize vuggy pore systems. The third example, from

the Utica Formation, Ohio, USA, demonstrates how image analysis can

efficiently characterize laminated to thin-bedded unconventional mudrock

reservoirs.

METHODOLOGY

For the Wafra and Toca studies, wireline logs—gamma ray (GR),

resistivity (RES), neutron porosity (NPHI), density (RHOB), photoelectric

Published Online: May 2017Copyright � 2017, SEPM (Society for Sedimentary Geology) 1527-1404/17/087-460/$03.00

absorption (PEF), caliper (CAL), and borehole image (FMI) were

available; in addition to core gamma ray and core plug analyses (porosity,

permeability, grain density, and saturation). Log-derived mineralogy,

porosity, and water saturation were calculated from these data. For the

Utica study, the only petrophysical data utilized were core gamma ray.

Because log depth differs from core depth, core-log integration requires

depth shifting to ensure that all comparable data are at the same depth.

WellCAD software was used for loading, depth shifting, and integration of

all log, core, and image data. The wireline logs were first loaded into the

document. The FMI logs were then loaded and depth shifted to match the

wireline logs. Next, the core gamma and core plug porosity data were

loaded and depth shifted to match the wireline GR log and log-derived

porosity. Lastly, images from the CT scans and core photographs were

loaded and further depth shifted as required to match the FMI log. This

integrated dataset allows quantitative comparison of images, logs, core

data, and geologic descriptions.

Color WL photographs of slabbed core were available for every study.

The core photographs are 32 bit (red, green, blue) RGB images at 72

pixels/inch (3 pixels/mm). For the Wafra and Toca studies, full-diameter

core CT scans, acquired at a resolution of 300 scans/ft. (1 scan/mm), were

available. These scans are 8 bit grayscale images, also at 72 pixels/inch (3

pixels/mm). This study used the outside circumference of the CT image for

analysis, because it could be directly compared with the borehole image for

depth shifting.

Photo-editing software was used to crop images from the core box

photographs and then mosaic the photographs into a continuous image.

The CT scans, generally in 3 ft. (0.9 m) lengths, were also compiled into a

continuous image. The continuous images from core photographs and CT

scans were then sliced into uniform 1 ft. (0.3 m) lengths in order to

generate digital log curves of the image analysis with a 1 ft. (0.3 m) vertical

resolution, similar to the log and core plug datasets. Because 72 pixels/inch

(3 pixels/mm) core photographs and CT scans were used for the analysis,

small-scale features , 9 pixels2 in area (e.g. , 0.002 inch2, , 1.0 mm2)

were filtered out from the analytical results. The images provided adequate

resolution for the macro-scale features of interest.

Quantitative image analysis of core photographs and CT scans for the

First Eocene and Toca studies was performed using ImageJ—a freeware,

public-domain program that was initially developed at the National

Institute of Health (NIH). ImageJ has an open software architecture; its

power is derived from user-written plugins and macros that are customized

to solve specific image processing and analysis applications. This study

utilized both publicly available freeware and a Chevron proprietary version

of ImageJ for image processing and analysis. A Macintosh spinoff version

of the NIH software, Image SXM, has customized features similar to those

utilized by this study from Chevron proprietary software. Image SXM is

available for free distribution and is documented in detail by Heilbronner

and Barrett (2014).

In CT scans, the density contrast between evaporite minerals, open vugs,

and the host dolostone can be distinguished due to the attenuation of X-

rays that pass through the material and are then filtered to create a

monochromatic image. Anhydrite attenuates the X-ray beam and displays

corresponding low grayscale (white to light gray) values on CT scans.

Conversely, open vugs do not impede the X-rays and display high

grayscale values (dark gray to black). Dolostone exhibits mid-range

FIG. 1.—Location map for Wafra Field and generalized stratigraphic column. First Eocene is the shallowest reservoir interval. Modified from Saller et al. (2014).

QUANTITATIVE IMAGE ANALYSIS FOR GEOLOGIC CORE DESCRIPTIONJ S R 461

grayscale values (medium gray). CT scan images from the Wafra and Toca

studies were segmented according to grayscale values using ImageJ

software. The area for each phase was then computed for each 1 ft. vertical

slice to quantify the mineralogy and vuggy porosity. The results are

presented as a log curve with a data point for every foot.

White-light photographs of core slabs were similarly analyzed using

ImageJ software. The images were first processed and corrected for

exposure. For the Wafra study, the color contrast between white anhydrite

nodules and gray-colored gypsum overgrowths was used to define hue,

saturation, and brightness (HSB) parameters to distinguish anhydrite from

gypsum. ImageJ software was used to segment the images according to the

HSB parameters. The area of each mineral phase was then computed for

each 1 ft. vertical slice. The results are presented as a log curve with a data

point for every foot.

Because oil stain rapidly fades after core is slabbed, WL (and UV)

photographs of freshly slabbed core provide better information on oil stain

than actual core viewing, which may be months after the core was slabbed.

In the Wafra cores, oil stain is the only dark-colored component, thus it can

be readily quantified on the basis of color. Color, as defined by HSB, was

used to segment the images, using ImageJ software, which then computed

the area of oil stain for each 1 ft. (0.3 m) vertical slice. The results are

presented as a log curve with data at a 1 foot vertical spacing.

The Utica Formation consists of light-colored, laminated to thin-bedded

limestones interbedded with dark mudrock. In this example, core

photographs were loaded into WellCAD software, where the image-

analysis application was used to extract a single log curve representing

median grayscale values at a specified vertical interval of 0.01 ft. (3 mm).

High grayscale values represent limestone and low values represent

mudrock. This dense vertical sampling captured laminated and thin-bedded

vertical heterogeneity that is difficult to assess with traditional hand-drawn

core descriptions, wireline logs, and standard-diameter core plugs.

QUANTITATIVE IMAGE ANALYSIS

Example 1: First Eocene Formation, Kuwait–Saudi Arabia

Description.—The First Eocene is the shallowest reservoir in Wafra

Field (Fig. 1). Reservoir geology and production are described by Saller et

al. (2014) and Meddaugh et al. (2007, 2011a, 2011b). The reservoir (Fig.

2) consists of cyclic peritidal dolostones with intercrystalline porosity.

Porosity and permeability values range up to 50% and 6000 mD.

Evaporites are present as dispersed and coalesced nodules and up to 5-ft-

thick (1.5 m) bedded units. Anhydrite is the dominant evaporite mineral;

gypsum occurs as a thin (0.4 inch, 1 cm thick) outer rind on evaporite

nodules.

Because of poor primary oil recovery (Champenoy et al. 2011; Rubin

2011), steamflooding in the First Eocene Wafra reservoir began in 2006

(Barge 2009; Meddaugh 2011b). Steam was injected into a zone 50 ft thick

(15 m) where porous dolostones are overlain by tight, finely crystalline

dolostone and bedded evaporite, which provide a vertical barrier to

steamflood (Fig. 2). The previously cored ‘‘Well A’’ was used to monitor

the thermal buildup (Figs. 3, 4) associated with steam injection (Meddaugh

et al. 2011a, 2011b). In the first three years, the temperature in the

steamflooded zone increased by nearly 2008C. To monitor the effects of

steamflood on the reservoir fluid and rock properties, ‘‘Well B’’ was drilled

in 2012, at a lateral distance of 70 ft. (21 m) away from the previously

cored Well A (Fig. 3). Core was acquired in order to petrographically and

petrophysically compare the pre- and post-steamflood cores, with the intent

of quantifying possible steamflood-induced changes in mineralogy,

FIG. 2.—Stratigraphic summary from Bachtel

(2014 unpublished). First Eocene Formation

consists of cyclic interbedded restricted platform

dolomitized peritidal facies. The steamflood test

interval occurs in porous subtidal peloid dolo-

packstones in upper Sequence 2 that are overlain

by tight finely crystalline dolomites from mud-

dominated intertidal facies and bedded anhydrite.

R.J. BARNABY462 J S R

reservoir quality, and fluid saturation and to evaluate the steamflood sweep

efficiency.

Comparison of the two closely spaced cores indicates that the

depositional facies, rock fabric, and diagenetic features are nearly identical.

In the interval above the steamflood zone, the two cores display similar

properties with respect to oil stain, saturation, mineralogy, and porosity. In

the steamflood zone, however, the post-steamflood core exhibits a

significant decrease in oil stain compared to the correlative interval in

the pre-steamflood core (Fig. 5). Evaporite nodules in the pre-steamflood

core consist of white masses of anhydrite with a medium gray outer rind,

up to 0.4 inch (1 cm) thick, composed of gypsum. In the steamflooded

core, evaporite nodules exhibit a tan oil stain and gypsum is absent.

Results.—CT scan images were segmented according to grayscale to

delineate total evaporite content (Fig. 6). The amount of visual evaporite

was calculated using ImageJ and presented as a log curve (Fig. 7). The

image-derived mineral volumes are consistent with the log-derived

mineralogy based on multimin analysis.

Because anhydrite is white and gypsum is medium gray, the two

minerals can be distinguished in core photographs (Figs. 8, 9). The two

minerals are also identified in thin sections using standard petrographic

techniques (Figs. 8–10). ImageJ was used to segment the core photographs

to delineate anhydrite and gypsum (Fig. 11). The amount of each mineral

phase was computed at 1 foot (0.3 m) intervals, generating a quantitative

curve for the amount of gypsum and anhydrite (Fig. 12). The image-

derived mineral volumes are consistent with log-derived mineralogy using

multimin analysis.

Image analysis indicates that the pre-steamflood core from Well A

locally contains up to 28% gypsum, with an average value of 12% over

the steamflood-equivalent zone, whereas little to no gypsum occurs over

this interval in the post-steamflood core from Well B (Fig. 13). Thin-

section petrography (Fig. 10) provides direct evidence of gypsum

dissolution. Intervals that exhibit gypsum dissolution display a

corresponding increase in porosity (Fig. 13). Core plugs and logs

indicate that porosity increased as much as 10–15% locally, with an

average of 2–3% porosity increase.

Comparison of the log-derived porosities for the pre- and post-

steamflood wells confirms an increase in porosity due to gypsum

dissolution (Fig. 14). In post-steamflood Well B, the porosities are skewed

to higher average and median values than the porosity values from the pre-

steamflood well. A cross plot of the log-derived total porosity (PHIT)

curves for the steamflooded zones confirms the interpretation that selective

dissolution of gypsum created porosity (Fig. 15). The two wells exhibit

similar porosities where only minor gypsum (, 7.5%) was present.

However, for the gypsum-rich lithologies (gypsum . 7.5%), the porosity

values in the post-steamflood well are up to 10–15% greater than in the

pre-steamflood well.

The amount of oil stain was computed from the core photographs using

ImageJ. Comparison of the core photographs and the segmented images

(Fig. 16) demonstrates how core-plug saturations will differ from image

analysis due to small-scale heterogeneity. Although both techniques yield

quantitative results, they are based on entirely different sample volumes:

1.5 inch (3.7 cm) core plugs vs. 1 ft. (0.3 m) core photographs. In general,

the results from the two techniques are consistent (Fig. 17).

Within the steamflooded reservoir, quantitative image analysis docu-

ments locally up to 95% reduction in oil stain, with an average of 66% oil-

stain reduction (Fig. 17). Core-plug oil saturation analyses are consistent

with these calculations. Core plugs from the steamflood zone indicate

locally up to 95% recovery, with an average of 76% recovery over the

interval, while log analyses indicate a slightly lower average of 57% oil

recovery (Fig. 17). Image analysis of oil stain from core photographs can

thus yield results comparable with log and core data.

Discussion.—Image analysis of core photographs indicates extensive

gypsum dissolution in Wafra Field due to steamflood, which caused a

corresponding increase in porosity. Quantitative image-derived estimates

of the amount of gypsum dissolution are supported by petrophysical core

and log data. These interpretations imply significant mobilization of

gypsum during steamflood, which is supported by reports of CaSO4 scale

buildup causing production problems. Thin-section petrography and image

analysis indicate that gypsum dissolution is confined to the outer periphery

of evaporite nodules, which could impact enhanced oil recovery by

channeling steamflood and reducing matrix sweep. This demonstrates that

image analysis has valuable high-resolution reservoir description capabil-

ities that are unmatched by other techniques.

Quantitative analysis of core photographs indicates that steamflood

caused, on average, a 66% reduction in oil stain (Fig 17). This is consistent

with log- and core-derived estimates of oil recovery ranging from 57% to

76%, respectively. Moreover, image analysis yields a high-resolution,

better than 0.05 inch (1 mm), characterization of the heterogeneous

distribution of residual oil (Fig. 16) than is possible using logs and

standard-diameter core plugs. This high-resolution characterization of oil

recovery is critical for evaluating the effectiveness of matrix sweep by

steamflood. It also provides a framework for further investigations of fine-

FIG. 3.—Base map for Wafra First Eocene steamflood test that initiated in 2006.

The design shows an inverted five-spot pattern, with the steam injector well in the

center flanked by four producers. Well Awas cored in 2004, prior to steamflood, and

served as an observation well to monitor temperature buildup during steam injection.

Post-steamflood Well B was cored in 2012 at a lateral distance of 70 ft. from the

previous core. From Barge et al. (2009).


FIG. 4.—Temperature-observation well (Well A) exhibiting reservoir heating due to steam injection through time, and the stratigraphic intervals of offset injector and

production wells. From Barge (2010) unpublished report.


scale geological heterogeneity using high-resolution CT plug scans, micro-

permeameter analysis, and detailed petrography.

Example 2: Cretaceous Toca Dolostones: Offshore Angola

Vuggy pore systems can create permeabilities that are orders of

magnitude greater than matrix porosity (Lucia 1999), and control reservoir

performance. Vuggy reservoirs are challenging to accurately describe using

wireline logs, core plugs, and hand-drawn geologic core descriptions.

While the neutron porosity (NPHI) and density (RHOB) logs quantify the

total volume of porosity, these logs cannot distinguish between matrix and

vuggy porosity. Core plugs tend to underestimate vuggy porosity due to

sampling bias, because of the difficulty in cutting intact plug samples from

vuggy rocks. Traditional hand-drawn geologic core descriptions must rely

on qualitative terms (e.g., rare, common, abundant) to describe the amount

of vuggy porosity.

An example from the Toca Formation, in the Congo Basin of offshore

Angola, serves to illustrate the application of image analysis in

characterizing vuggy pore systems. An overview of Toca geology is

provided by Harris (2000). Toca reservoirs consist of vuggy dolomitized

skeletal grainstones and packstones. Abundant vugs, up to 10 cm or more

in diameter, are evident in CT scans, core photographs, thin sections, and

borehole images (Figs. 18–20).

This study used CT scans to quantify the vuggy porosity (Fig. 19). Core

photographs and thin-section scans could also be used to quantify vugs.

These methods, however, investigate a smaller volume of rock than the

FIG. 5.—Correlative cored intervals from pre-

and post-steamflood wells. Post-steamflood core

exhibits less oil stain than pre-steamflood core due

to steam-induced oil sweep. Note that the

evaporite nodules in the pre-steamflood core have

a different appearance compared with the post-

steamflood core. In the pre-steamflood core, white

anhydrite evaporite nodules have an outer rind of

medium gray gypsum, whereas gypsum is absent

in post-steamflood evaporite nodules, which

exhibit a tan oil stain.


full-diameter core CT scans. The CT scans were examined to eliminate

coring-induced breakage and fractures from the analysis. Using ImageJ

software, the area of the vugs was calculated (Fig. 19). The computed vug,

presented as a log curve (Fig. 20), is consistent with the log-derived total

porosity (PHIT), indicating that the porosity in this interval is dominated

by vugs rather than by matrix porosity. Core-plug porosity measurements

generally underestimate vuggy porosity (Fig. 20) due to sampling bias,

because it is impossible to acquire intact core plugs from intervals with

large vugs. Quantitative image analysis thus provides the best technique to

accurately characterize vuggy pore systems from core.

Example 3: Thin-Bedded Unconventional Reservoir: Utica Formation,

USA

Laminated to thin-bedded mudrocks pose a challenge to petrophysical

log analysis because bed thickness is below the resolution of most wireline

logs. Moreover, it is difficult to obtain representative standard-diameter

core-plug samples from thin beds and laminae. Detailed characterization of

the intercalated lithologic units using traditional hand-drawn core

description techniques is labor-intensive and prone to error. Small errors

in the measured depth will place the beds and bed boundaries at the

incorrect depth. Moreover, it is challenging to consistently describe every

thin bed and lamina, especially in a long core that requires days of effort to

describe.

Because of the color contrast in alternating thin beds and laminae in

most mudrock successions, image analysis is an excellent application to

characterize these reservoirs. An example from the Utica Formation, in

Ohio, USA, exhibits the fine-scale layered heterogeneity typical of these

interbedded light gray skeletal limestones and dark gray, organic-rich

mudrocks (Fig. 21). A general background to the geology of the Utica

Formation is available from King (2017).

For this study, the WellCAD image module was used to extract a log

curve of median grayscale values sampled at 0.01 ft. (3 mm) intervals. The

computed grayscale curve (Fig. 21) is similar to a grain-size profile drawn

by a geologist during traditional, hand-drawn core description. The

grayscale curve represents interbedded units of clean limestone and

mudrock. The computer-generated profile is more precise than a hand-

drawn description and eliminates hours (or days) of meticulous observation

and description. The initial computer-generated curve can be manually

edited as required during the core viewing.

FIG. 6.—CT core scan (3608 full-diameter outer

circumference) on left. High-density evaporite

nodules cores appear as white. Segmented image

on right was created by assigning a label to each

pixel based on grayscale value, pixels with

grayscale values between 180 and 255 identified

as evaporite (anhydrite and gypsum) depicted in

black on right image. Total area of evaporites in

this 1-ft.-long core calculated at 57% using

ImageJ software.


QUANTITATIVE CORE DESCRIPTION: ANALYSIS OF CORE DATA QUALITY

An important advantage of quantitative core description is that it

facilitates a review of the core data quality. Errors in core datasets are quite

common. For example, core depths can be mislabeled, lengths of core may

be flipped or misplaced, cores are sometimes dropped and pieced back

together by inexperienced staff, and sample points can be incorrectly

recorded. Sometimes errors are created before the full-diameter cores are

CT scanned, sampled, analyzed, and photographed. Errors also are

introduced when cores are slabbed, boxed, labelled, and photographed.

Core mishandling by geoscientists, unfortunately, is another source of error

for subsequent core descriptions.

With traditional hand-drawn core descriptions, errors in the core

database may be difficult to recognize. Often, geologists describe core

straight from the core boxes, implicitly assuming that the core was properly

handled and the core boxes were correctly labeled. Because hand-drawn

core descriptions tend to be qualitative and core-log integration is

imprecise, errors in the core database may not be identified during the

core description. I have seen many core descriptions that failed to

recognize that pieces or sections of the core were out of place or that

sample analyses were reported at the wrong depths.

Quantitative core description requires precise depth matching of log and

core datasets. By depth-shifted core images and core data to match the

wireline logs and borehole images, errors and mismatches become evident.

Using this workflow, I have identified errors in approximately half of the

cores that I have examined. Fortunately, such errors can be readily

identified and rectified. Two examples serve to illustrate problems common

in core datasets.

The first example is the post-steamflood core, Well B, from Wafra Field.

When core images from the CT scans and slabbed photographs were

depth-shifted to match the logs, log analysis, and borehole images, it

became evident that the core-log data could not be reconciled at the depth

interval of 1073–1079 ft. (Fig. 22). Examination of the core slabs and butts

indicates that two core pieces, 1073–1075.6 ft. and 1076–1078.6 ft., were

accidentally switched and mislabeled, with the additional complication that

a 0.4 ft. pieces at the bottom of each length were correctly labeled (Fig.

23). This error occurred prior to CT scanning, core gamma-ray analysis,

and plug sampling of the full-diameter core. The error persisted after the

FIG. 7.—Comparison of total evaporite derived

from CT scan image versus multimin-derived

petrophysical analysis. The results from image

analysis are consistent with petrophysical-based

interpretation.

!FIG. 8.—Petrography of evaporite nodule from pre-steamflood Well A. A) Core photograph; evaporite nodule consists of white anhydrite, with outer rim of medium gray

gypsum. B, C) Paired plane-polarized-light and cross-polarized light photomicrographs. Center of evaporite nodule dominated by randomly oriented laths of anhydrite (high

relief and high birefringence), outer margin of nodule dominated by blocky crystals of gypsum (low order gray birefringence) with scattered anhydrite inclusions. There is no

evidence of gypsum or anhydrite dissolution. Note local coarse crystals of calcite (cc) stained pink by alizarin Red S along outer periphery of nodule. This calcite was

interpreted by Saller et al. (2014) to represent biogenic SO4 reduction associated with oil degradation prior to reservoir development. D) Core photograph; evaporite nodule

core consists of white anhydrite, with medium gray outer rim composed of gypsum. E, F) Paired plane-polarized-light and cross-polarized light photomicrographs. Outer

margin of evaporite nodule consists of gypsum with scattered inclusions of anhydrite, grading into anhydrite-dominated center of nodule. There is no evidence of gypsum or

anhydrite dissolution. Calcite (cc) is stained pink by alizarin Red S along outer periphery of nodule.


FIG. 9.—Petrography of evaporite nodule from post-steamflood Well B above steamflooded zone. A) Core photograph; evaporite nodule cores consist of white anhydrite,

with outer rim composed of medium-gray gypsum. B) Cross-polarized light photomicrograph. Center of evaporite nodule is dominated by randomly oriented laths of

anhydrite, outer margin is dominated by blade-like crystals of gypsum with scattered anhydrite inclusions. There is no evidence of gypsum or anhydrite dissolution. C) Core

photograph; evaporite-nodule core consists of white anhydrite, with outer rim of medium-gray gypsum. Light tan material is styrofoam used to stabilize broken core pieces. D)

Cross-polarized light photomicrograph. Center of evaporite nodule is dominated by randomly oriented laths of anhydrite. Outer margin of nodule, adjacent to host rock, is

dominated by blocky crystals of gypsum with scattered anhydrite inclusions. There is no evidence of gypsum or anhydrite dissolution.

!FIG. 10.—Petrography of evaporite nodule from Well B steamflooded interval. A) Core photograph; chalky white outer rind corresponds to leached gypsum with residual

anhydrite. Anhydrite-nodule cores exhibit light tan oil stain. B, C) Plane-polarized-light photomicrographs. Outer periphery consists of residual anhydrite laths; matrix

gypsum was completely dissolved, creating porosity (filled with blue-dyed epoxy), whereas the anhydrite-rich evaporite nodule core remains unaltered. D) Core photograph;

chalky white outer rim corresponds to leached gypsum with residual anhydrite. Thin rim of remnant gypsum indicates that gypsum dissolution began along the outer

periphery of evaporite nodules, moving towards the nodule center through time. E, F) Paired plane-polarized-light and cross-polarized light photomicrographs. Magnified

view of Part D showing thin rim of remnant gypsum (low-order gray birefringence), dividing unaltered anhydrite (high relief and high birefringence) in upper portion of

photograph from leached gypsum filled by blue-dyed epoxy in lower photograph.


core was slabbed and photographed. Consequently, the entire core dataset

over this 6 ft. interval—CT scans, core photographs, core gamma ray, and

core plug analyses—had incorrect reported depths. These errors were not

detected by geologists who previously described this core. By rearranging

the core pieces to the correct positions (Fig. 23) and editing the image and

core analysis data to the proper depths, we can see that there now is an

excellent match between the core and log data (Fig. 24).

The second example of errors in a core dataset is from the Utica

Formation. As described above, the grayscale curve computed from the

core photographs (Fig. 21) represents interbedded units of light gray

limestone and dark gray mudrock. Because clean limestones have low

gamma-ray values and dark mudrocks have high gamma-ray values, the

grayscale curve should be comparable to the core gamma-ray log.

The core gamma data, as reported, are shown as the red log curve (Fig.

25). This curve appears to be out of phase with the core image—dark gray

mudrocks that have high gamma-ray signatures appear to correspond to

lower values in the core gamma-ray log and limestones that have low

gamma-ray signatures appear to correspond to higher values in the core

gamma-ray. This apparent mismatch between the core photos and core

gamma arises from core gamma-ray acquisition procedures. The

scintillometer tool begins measurement of gamma-ray emissions at the

bottom of the core and records data as the tool is slowly moved upward

relative to the core. A 1 ft. (0.3 m) moving average is typically reported—

beginning after the first 1 foot (0.3 m) of data are recorded. For example, if

the bottom of the core is 10,000 ft. (3048 m), the first reported value will

be at 9,999 ft. (3047.7 m) and it will represent the average value of the

interval between 9,999 and 10,000 ft. (3.047.7–3048.0 m).

By shifting the core gamma-ray curve downward by 1 ft. (0.3 m), there

is a better match between the core photos and the gamma data, as shown by

the green log curve (Fig. 25). Although this depth shift improves the

overall depth match, it still will not be perfect because of the core gamma-

ray averaging techniques. In thin-bedded reservoirs, the most accurate

technique to depth match core to log data is to tie the core images (CT

scans, photographs) to borehole images.

This mismatch between core depth and the core gamma-ray log, as

reported, is generally not recognized during the process of traditional hand-

drawn core description and petrophysical analysis. Traditionally, the core

gamma-ray log provides the standard for depth shifting core data to match

wireline logs. However, as this example shows, this practice will result in a

mismatch between the core and log data. Core plug data will be offset from

the wireline logs by 1 ft., a significant error in these laminated to thin-

bedded mudrock reservoirs.

FIG. 11.—Image analysis of core photographs to calculate volume of evaporite minerals. A) Core photograph, each slice is 1 ft. B) Segmented image; black represents

anhydrite, blue is gypsum, brown is background dolomite. C) Calculated anhydrite volume for each 1 ft. slice. D) Calculated gypsum volume for each 1 ft. slice.


FIG. 12.—Comparison of visual mineralogy volumes derived from image analysis with computed mineral volumes from petrophysical multimin analysis. The two different

techniques yield consistent results, although image analysis provides higher-resolution information of geologic heterogeneity.


FIG.13.—

Comparisonofmineralogyandporosity

betweenpre-steam

floodWellAandpost-steam

floodWellB.Intervalthatisshownisconfined

tosteamfloodzonein

WellBandcorrelativeintervalin

WellA.PHIT

is

log-derived

totalporosity.WellA

containsgypsum,whereasWellB

does

not.Thepaucity

ofgypsum

inWellBcorrespondsto

anincrease

inplugandlogporosity,recordinggypsum

dissolutionduringsteamflood.


SUMMARY AND CONCLUSIONS

Quantitative geological descriptions can be readily derived from

routine core images using free, open-source software (ImageJ) and

widely used commercial software (WellCAD). This technology enables

geologists to generate digital core descriptions that are fully integrated

with wireline logs, images from borehole and core, and core analyses.

Image analysis efficiently captures fine-scale geologic heterogeneity that

is difficult to resolve using standard log and core-plug data and traditional

hand-drawn core descriptions. Image analysis and petrophysical integra-

tion is performed using a computer to generate a preliminary description

prior to the actual core viewing. This optimizes the time expended

describing core at offsite core-viewing facilities, which may require travel

to remote locations with limited time available. Lastly, quantitative core

description facilitates the critical evaluation of core-based data for

possible errors.

This paper describes three examples of image-analysis-generated core

descriptions from carbonate rocks. Whole-core CT scans and slab core

photographs from dolostones in the First Eocene Formation in Wafra

Field provide quantitative data on the impact of steamflood to the

mineralogy, porosity, and oil saturation of the reservoir. Image analysis

documents that steamflooding caused gypsum dissolution, consistent

with log and core data that indicate a paucity of gypsum in the

steamflooded interval, accompanied by a corresponding increase in

porosity. Image analysis records an average of 66% reduction in oil stain

in the steamflood intervals, consistent with log and core saturation

analyses. Moreover, image analysis allows a high-resolution understand-

ing of steamflood-induced changes to the reservoir—beyond the

resolution of log and core-plug data. Image analysis demonstrates that

gypsum dissolution is confined to the margins of the evaporite nodules

FIG. 14.—Histogram comparison of log-de-

rived porosity between Well A and Well B in

steamflood zone. Post-steamflood porosity does

not exhibit as many low values as the pre-

steamflood values, the porosity values exhibit a

more narrow range, and the average and median

values are greater.

FIG. 15.—Cross plot of log-derived porosity between Well A and Well B confined

to steamflood zone and correlative interval. For intervals with minor gypsum

(, 7.5%), the porosity values are similar. Conversely, for intervals with abundant

gypsum (. 7.5%), the post-steamflood Well B exhibits greater porosity. Steamflood

thus causes an increase in porosity in the initially tighter, gypsum-rich lithologies,

whereas the more porous, dolomitic lithologies are unaltered.


FIG.16.—

Core

photographsandsegmentedim

ages

ofvisualoilstain.Eachpixelisassigned

avalue(oil-stained

ornotoil-stained),based

onhue,saturation,andbrightnessthresholdsusingIm

ageJ

software.Eachcore

piece

is1ft.in

length.Plugoil-saturation(S

o)values

arecompared

withim

ageanalysisofoil-stain

volume.In

ahighly

heterogeneousreservoir,core

plugsdonotadequatelyrepresentthelarger-scalereservoirproperties,

whereasim

ages

providealarger

sample

size

that

bettermatches

wirelinelogdata.

Moreover,im

ageanalysisprovides

higher-resolutiondataofthedistributionofoilstain.


FIG. 17.—Comparison of Well A (pre-steamflood) and Well B (post-steamflood) cores for visual oil-stain, plug saturation, and log-derived saturation. From left to right 1)

Well A core photograph, image segmented according to visual oil-stain; visual oil-stain volume (0–100%, increasing to right), and plug oil saturation (So) and water saturation

(Sw). 2) Well B core photograph, image segmented according to visual oil-stain; visual oil-stain volume, and plug So and Sw. Comparison: D (Well Avalues – Well B values)

between these two wells with respect to visual oil-stain, plug So, and log-derived oil saturation. Steamflooded zone in Well B exhibits less oil stain than correlative interval in

Well A, suggesting an average of 66% oil recovery, consistent with core and log data indicating 76% and 57% recovery, respectively.


FIG. 18.—Images of vuggy porosity in dolomitized coquina facies. CT scan on left is image of 3608 full-diameter core outer-circumference. Other samples with smaller-

scale vugs exhibited by core-slab and thin-section images (impregnated with blue-dyed epoxy).


and that oil sweep is highly variable due to small-scale geological

heterogeneity created by patchy evaporite distribution.

Quantitative characterization of vuggy porosity from carbonates has

long eluded the capabilities of traditional hand-drawn core description.

Toca Formation vuggy dolostones provide an example to demonstrate how

image analysis can characterize vuggy pore systems. The vugs range up to

4 in (10 cm) in size, too small to be individually resolved by wireline logs

and too large to be characterized by standard-diameter core plugs. Whole-

core CT scans provide a means to accurately quantify the contribution of

vugs to the total porosity. In this example, vugs account for nearly all of the

porosity that is identified from the wireline logs, consistent with thin-

section petrography.

Thin-bedded to laminated unconventional reservoirs such as the Utica

Formation are challenging to petrophysically characterize because bed

and lamina thickness is below the resolution of wireline logs and

standard-diameter core plugs. Traditional geological characterization of

these finely layered, heterogeneous reservoirs by hand-drawn core

description is laborious and unlikely to generate quantitative data. The

color contrast due to interbedded different lithologies, however, enables

image analysis of core photographs to efficiently generate quantitative

geologic descriptions.

These examples demonstrate that image analysis is a viable technology

that should be more widely utilized by geologists. The time expended to

learn this technology is compensated by the ability to efficiently generate

quantitative core descriptions. Lastly, traditional hand-drawn geologic core

descriptions have limited utility for coworkers in other disciplines, such as

petrophysicists, modelers, and reservoir engineers, who require digital

geologic descriptions and interpretations.

ACKNOWLEDGMENTS

S.L. Bachtel, Chevron Energy Technology, described depositional facies and

interpreted the stratigraphy for the two study cores. R. Salazar-Tio, also of

Chevron Energy Technology, assisted with image processing and analysis. I

thank the Partitioned Zone Saudi Arabian Chevron and Chevron Energy

Technology in granting permission to publish this work. I thank Advanced

Logic Technology for use of a WellCAD license to prepare the figures. The

manuscript benefited from reviews by M. Minzoni, Dave Pivnik, Leslie Melim,

and two unnamed JSR reviewers.

FIG. 19.—Work flow for quantitative image analysis of vuggy porosity from CT scans. Open void space in CT scans has low grayscale (black) values and is readily

distinguished from host dolostone. Induced core breakage is identified (middle image) and eliminated from analysis, showing vug porosity in red. CT scans were next sliced

into 1 ft. images for image analysis with example output summary from ImageJ software.


FIG. 20.—PHIT (total log-derived porosity) compared with image analysis of vuggy porosity (red) indicates that vugs account for nearly all of the porosity in this interval of

dolomitized coquina grainstones and packstones, consistent with thin-section petrography.


FIG. 21.—Utica Formation composed of light

gray skeletal limestone interbedded with dark gray

mudrock. Grayscale profile, computed using

WellCAD software based on core photographs,

generates a lithologic profile similar to geological

hand-drawn core descriptions. This profile can be

further edited during the core viewing.


FIG.22.—

Depth-shiftedCTscan

andcore

photographdonotmatch

borehole

imageorpetrophysicalanalysisat

recorded

depths1073–1079ft.(327–329m),indicatingthat

thereareproblemsin

thecore

dataset.


FIG.23.—

Core

boxphotographsshowsthattwopiecesofcoreweretransposedandmislabeled.Thiserrororiginated

soonafterthecorewas

acquired,either

atthewellsiteorwhen

thefull-diameter

corewas

cutinto

3ft.

lengths,boxed,andlabeled.Thiserrorcompromises

theentire

core

dataset—full-diameter

CTscans,core

gam

ma-ray,

core-plugsamples,core

photographs,andpreviousgeologic

core

descriptions.


FIG.24.—

After

correctingandeditingcore

images,therenow

isan

excellentmatch

betweendepth-shiftedCTscan

andcore

photographwithborehole

imageandpetrophysicalanalysis.


FIG. 25.—Utica Formation computed grayscale profile (from Fig. 21). Because light gray limestones have lower core gamma-ray values than dark gray mudrocks, the

grayscale profile should correspond to the core gamma-ray log. The gamma-ray log, as reported (red curve) displays lower values for mudrocks, for example at 6221, 6224,

and 6226 ft. and higher values for limestones, for example at 6219, 6223, and 6225 ft. This depth mismatch is due to core gamma-acquisition statistics (see text). Shifting the

core gamma downward by 1 ft. (green curve) exhibits a better match between image and core gamma data.


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Received 10 March 2016; accepted 6 February 2017.


quantitative image analysis for geologic core description

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